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pythonpapersurveyasset-pricingafter-work

Introducing From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing for after-work quants

Ye, Goswami, Gu, Uddin & Wang (2024) survey how ML reshapes empirical asset pricing: factors → flexible g(x), portfolios, GNNs, RL, and the usual overfitting/explainability traps. Reading map. Not investment advice.

pythonpapermachine-learningasset-pricingafter-work

Introducing Empirical Asset Pricing via Machine Learning for after-work quants

Gu, Kelly & Xiu (2019) bake off ML methods (trees, nets, and more) for the equity cross-section. Plain-language intro. Not investment advice.

pythonpaperasset-pricingdeep-learningafter-work

Introducing Deep Learning in Asset Pricing for after-work quants

Chen, Pelger & Zhu (2019) estimate a deep SDF / pricing kernel from characteristics—end-to-end asset pricing without a hand-built factor zoo. Plain-language intro. Not investment advice.